About 1001

1001 builds AI-powered operational intelligence for the world's most complex, data-heavy environments. We turn fragmented data into a live, unified model of operations and use it to drive better decisions and solve high-stakes problems.

Our work sits inside government and large enterprises, in environments defined by critical operations and messy, real-world data. Our engagements start with forward-deployed teams embedded in the customer environment. They work on real data, build quickly, and iterate until the system proves itself, then scale it across the organization.

The company is backed by Lux Capital, General Catalyst, CIV, Hanabi, Sanabil, and 9Yards, with angels including Chris Ré, Amjad Masad, Karim Atiyeh, Kareem Amin, and Russell Kaplan.

About the role

You will build the models behind our optimizer and the systems around it, helping set the technical ceiling of our applied research stack. This is applied research, where success is measured by production impact rather than benchmark numbers or accepted papers.

You will lead work on reinforcement learning-guided optimization, world models, and simulation-driven learning for physical-world problems where data is messy, constraints are hard, and decisions carry real cost. You will design, train, and validate models, then turn them into artifacts the engineering team can ship.

You will stay close to current research and bring frontier techniques into our production stack. You will implement your own work rather than handing off ideas. The output must be rigorous and practical enough for a small applied team to build on directly.

What you will work on

• Design, train, and validate the machine learning models that power our optimizer and adjacent systems.
• Lead research on reinforcement learning-guided optimization, world models, and simulation-driven learning for our use cases.
• Own research threads from initial idea through to validated models that the engineering team can ship.
• Bring frontier techniques from the literature into a production stack.

Requirements

• First-author publications at top venues such as NeurIPS, ICML, ICLR, AAAI, or JMLR, or an equivalent research record.
• A research record rigorous enough for a small applied team to build on.
• The ability to implement your own research and ship working code, not just papers.
• A genuine interest in applied industrial and physical-world problems where production impact defines success.
• Fluency with the current literature and sound judgment about which techniques to bring into production.

Nice to have

• Experience applying reinforcement learning, model-based planning, or simulation to industrial problems.
• A background in operations research.

Working at 1001

We take on high-stakes problems in environments where mistakes carry real consequences. That demands an uncompromising bar, real speed, and systems that hold up under live operations. The people who thrive here set that bar for themselves and keep raising it. They own outcomes from end to end, bring rigor to everything they do, and lift everyone around them.